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Gemma 4 26B A4B vs Qwen3.7 Plus

Compare Gemma 4 26B A4B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.

Compare Gemma 4 26B A4B vs Qwen3.7 Plus live

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GoogleGemma 4 26B A4B
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QwenQwen3.7 Plus
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Models in this comparison

Gemma 4 26B A4B vs Qwen3.7 Plus Comparison Table

Evals updated August 20, 2026Pricing updated August 24, 2026

PropertyGemma 4 26B A4BQwen3.7 Plus
OrganizationGoogleQwen
Categoryopenclosed
Modalitymultimodal
Release DateApr 2026
Context Window256K
Parameters25.2B
LicenseApache 2.0
Pricing per 1M tokens
Input $/1M$0.070$0.320
Output $/1M$0.340$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question Answering
Document Question Answering
Image Tagging
Multi-Label Classification
Vision Language
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
OverallNot evaluated
67.4%
Avg cost / sample$0.0008
Avg speed / sample7.01s
By task
Object Detection
60.1%
$0.0013
Counting
50.0%
$0.0004
Identification
84.4%
$0.0003
OCR
86.5%
$0.0009
Data Extraction
83.5%
$0.0004
Reasoning (low)
39.7%
$0.0003
Reasoning (high)
68.2%
$0.0043

Gemma 4 26B A4B vs Qwen3.7 Plus: Overview

Gemma 4 26B A4B

Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.

For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.

Qwen3.7 Plus
No description available

Frequently Asked Questions

Gemma 4 26B A4B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.